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However, the null hypothesis will always fall given enough data. The smaller effect you wish to identify, the larger the required sample size will be and at some point, the sample size required may be greater than the population at hand, which makes the experiment impossible. There is no need to spend endless hours writing grant applications, thoughtfully designing experiments, tirelessly recruiting participants, and then chasing follow-up data to reduce attrition-if all you want to know is if an intervention has an effect, then the answer is Yes - all interventions have an effect and you can prove it using P value dichotomization as long as you have enough data. The pendulum has unfortunately swung, as statistical significance has become the arbiter in many scientific disciplines, taking precedence over real-world impact of results, model critique, data quality, etc.īut is it not of the upmost importance to science to have a method to decide if an intervention has an effect? The answer is, to some rather surprisingly, a resounding No. It is remarkable that less than 60 years ago Hill wrote: “Fortunately I believe we have not yet gone so far as our friends in the USA where, I am told, some editors of journals will return an article because tests of significance have not been applied”. The (ab-)use of P values-the great divider of evidence, minds, and hearts-is, despite a great deal of critique, still going strong.
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Finally, we discuss the remedy in the form of Bayesian methods, where uncertainty leads and which allows for continuous decision making to stop or continue recruitment, as new data from a trial is accumulated. We show plots which we hope would intuitively highlight that all assessments of evidence will fluctuate over time. We also show the fickleness of P values, how they may one day point to statistically significant results and after a few more participants have been recruited, the once statistically significant effect suddenly disappears. The job of science should be to unearth the uncertainties of the effects of treatments, not to test their difference from zero. In this viewpoint we discuss that if testing the null hypothesis is the ultimate goal of science, then we need not worry about writing protocols, consider ethics, apply for funding, or run any experiments at all-all null hypotheses will be rejected at some point-everything has an effect. However, the endeavor to dichotomize evidence into significant and nonsignificant has led to the basic driving force of science, namely uncertainty, to take a back seat. When should a trial stop? Such a seemingly innocent question evokes concerns of type I and II errors among those who believe that certainty can be the product of uncertainty and among researchers who have been told that they need to carefully calculate sample sizes, consider multiplicity, and not spend P values on interim analyses.